Fig. 1
From: Active learning for accelerated design of layered materials
Workflow for optimal structure and property prediction. First, structure files for a family of N-layered materials are created and uploaded to the Materials Project (MP) database. Second, the MP infrastructure performs all DFT calculations, and subsequently, transport calculations using BoltzTraP code are performed. A snapshot of the material property data computed by MP database is pictured, along with the thermoelectric parameters computed by BoltzTraP. Third, a numerical feature vector is assigned to uniquely represent each structure. Fourth, and finally, machine learning techniques are applied to the data to make predictions for either a material property or an optimal structure
